The use of Machine Learning (ML) has become increasingly prevalent in many industries, providing companies with new sources of benefits. However, the life insurance industry has been slow to adopt ML due to the efficiency of traditional statistical models for risk assessment. This reluctance may lead insurers to face difficulties in assessing the value of artificial intelligence. The evolution of the life insurance industry over time highlights the potential benefits that ML can bring by unlocking data value. To address this issue, a group of authors including Antoine Chancel, Laura Bradier, Antoine Ly, Razvan Ionescu, Laurene Martin and Marguerite Sauce have published a paper titled "Applying Machine Learning to Life Insurance: some knowledge sharing to master it". The paper reviews traditional actuarial methodologies for survival modeling and extends them with ML techniques. It points out differences between regular machine learning models and emphasizes the importance of specific implementations to deal with censored data using machine learning models family. In addition to the article, a Python library has been developed that adjusts different open-source ML algorithms to adapt to the specificities of life insurance data such as censoring and truncation. These models can be easily applied from this SCOR library to accurately model life insurance risks. The authors suggest that by incorporating ML into their practices, insurers can unlock valuable insights from their data sets and improve risk assessments. The implementation of these techniques could lead to more accurate predictions about policyholder mortality rates and better pricing strategies for policies. Overall, this paper provides an insightful guide for insurers looking to incorporate ML into their operations while highlighting its potential benefits in improving risk assessments within the life insurance industry.
Machine Learning (ML) is a way for computers to learn and make predictions based on data. It's used in many industries, but not as much in life insurance yet. Life insurance companies use math to figure out how risky it is to insure someone, but they haven't been using ML much yet. Some people wrote a paper that talks about how ML could be helpful for life insurance companies. They made a special computer program called a Python library to help with this. If life insurance companies start using ML more, they might be able to make better guesses about who will live a long time and who won't, which would help them set prices for their policies.
Definitions
- Machine Learning: A way for computers to learn from data and make predictions. - Industry: A group of businesses that do similar things. - Risk assessment: Figuring out how likely something bad is to happen. - Actuarial methodologies: Math techniques used by insurance companies to figure out risk. - Censored data: Data where some information has been removed or hidden. - Python library: A special computer program that can help with Machine Learning. - Mortality rates: How many people die in a certain group over a certain amount of time. - Pricing strategies: Deciding how much money something should cost based on different factors.
Applying Machine Learning to Life Insurance
Applying Machine Learning to Life Insurance: Some Knowledge Sharing to Master It
The use of Machine Learning (ML) has become increasingly prevalent in many industries, providing companies with new sources of benefits. However, the life insurance industry has been slow to adopt ML due to the efficiency of traditional statistical models for risk assessment.
This reluctance may lead insurers to face difficulties in assessing the value of artificial intelligence. The evolution of the life insurance industry over time highlights the potential benefits that ML can bring by unlocking data value. To address this issue, a group of authors including Antoine Chancel, Laura Bradier, Antoine Ly, Razvan Ionescu, Laurene Martin and Marguerite Sauce have published a paper titled "Applying Machine Learning to Life Insurance: some knowledge sharing to master it".
The paper reviews traditional actuarial methodologies for survival modeling and extends them with ML techniques. It points out differences between regular machine learning models and emphasizes the importance of specific implementations to deal with censored data using machine learning models family.
In addition to the article, a Python library has been developed that adjusts different open-source ML algorithms to adapt to the specificities of life insurance data such as censoring and truncation. These models can be easily applied from this SCOR library to accurately model life insurance risks.
The authors suggest that by incorporating ML into their practices, insurers can unlock valuable insights from their data sets and improve risk assessments.
Machine Learning permeates many industries, which brings new source of benefits for companies. However within the life insurance industry, Machine Learning is not widely used in practice as over the past years statistical models have shown their efficiency for risk assessment. Thus insurers may face difficulties to assess the value of the artificial intelligence. Focusing on the modification of the life insurance industry over time highlights the stake of using Machine Learning for insurers and benefits that it can bring by unleashing data value. This paper reviews traditional actuarial methodologies for survival modeling and extends them with Machine Learning techniques. It points out differences with regular machine learning models and emphasizes importance of specific implementations to face censored data with machine learning models family. In complement to this article, a Python library has been developed. Different open-source Machine Learning algorithms have been adjusted to adapt the specificities of life insurance data, namely censoring and truncation. Such models can be easily applied from this SCOR library to accurately model life insurance risks.
